ISC
Catherine Walsh
5/27/2020
library(tidyverse)## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.2.1 ✓ purrr 0.3.3
## ✓ tibble 2.1.3 ✓ dplyr 0.8.3
## ✓ tidyr 1.0.0 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.4.0## ── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(patchwork)
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithslibrary(reticulate)
reticulate::use_python("/Users/catherinewalsh/miniconda3")
load('data/behav.RData')
load('data/split_groups_info.RData')
load('data/ISC_data.RData')
se <- function(x) {
sd(x,na.rm=TRUE)/sqrt(length(x[!is.na(x)]))
}Temporal ISC
Want to look at which regions show a load effect.
load_effect_LOO <- fisherz(high_correct_ISC_LOO) - fisherz(low_correct_ISC_LOO)
load_effect_LOO[load_effect_LOO == -Inf] <- NA
load_effect_LOO[load_effect_LOO == Inf] <- NA
LOO_results <- data.frame(t=matrix(nrow=297),p=matrix(nrow=297), sig_05=matrix(nrow=297), sig_corrected=matrix(nrow=297))
# one sample t test against 0
for (region in seq.int(1,297)){
temp <- t.test(load_effect_LOO[,region])
LOO_results$t[region] <- temp$statistic
LOO_results$p[region] <- temp$p.value
if (temp$p.value < 0.05){
LOO_results$sig_05[region] <- TRUE
} else {
LOO_results$sig_05[region] <- FALSE
}
if (temp$p.value < 0.05/297){
LOO_results$sig_corrected[region] <- TRUE
} else {
LOO_results$sig_corrected[region] <- FALSE
}
}
print("Regions that show a load effect (corrected for multiple comparisons): ")## [1] "Regions that show a load effect (corrected for multiple comparisons): "labels[LOO_results$sig_corrected,]## [1] "7Networks_LH_Vis_5" "7Networks_LH_Vis_11"
## [3] "7Networks_LH_Vis_18" "7Networks_LH_Vis_21"
## [5] "7Networks_LH_Vis_22" "7Networks_LH_Vis_23"
## [7] "7Networks_LH_Vis_27" "7Networks_LH_DorsAttn_Post_5"
## [9] "7Networks_LH_DorsAttn_Post_7" "7Networks_LH_DorsAttn_Post_9"
## [11] "7Networks_LH_DorsAttn_Post_11" "7Networks_LH_DorsAttn_Post_12"
## [13] "7Networks_LH_DorsAttn_Post_13" "7Networks_LH_DorsAttn_FEF_3"
## [15] "7Networks_LH_SalVentAttn_FrOper_9" "7Networks_LH_SalVentAttn_PFCl_1"
## [17] "7Networks_LH_SalVentAttn_Med_7" "7Networks_LH_Cont_Par_2"
## [19] "7Networks_LH_Cont_Par_6" "7Networks_LH_Cont_PFCl_2"
## [21] "7Networks_LH_Cont_PFCl_4" "7Networks_LH_Cont_PFCl_7"
## [23] "7Networks_LH_Cont_PFCl_8" "7Networks_LH_Cont_PFCmp_1"
## [25] "7Networks_LH_Default_Temp_1" "7Networks_LH_Default_Temp_12"
## [27] "7Networks_LH_Default_Temp_15" "7Networks_LH_Default_PFC_1"
## [29] "7Networks_LH_Default_PFC_2" "7Networks_LH_Default_PFC_5"
## [31] "7Networks_LH_Default_PFC_9" "7Networks_LH_Default_PCC_6"
## [33] "7Networks_LH_Default_PCC_7" "7Networks_RH_Vis_6"
## [35] "7Networks_RH_Vis_11" "7Networks_RH_Vis_15"
## [37] "7Networks_RH_Vis_19" "7Networks_RH_Vis_22"
## [39] "7Networks_RH_Vis_26" "7Networks_RH_DorsAttn_Post_4"
## [41] "7Networks_RH_DorsAttn_Post_9" "7Networks_RH_DorsAttn_Post_11"
## [43] "7Networks_RH_DorsAttn_Post_12" "7Networks_RH_DorsAttn_Post_15"
## [45] "7Networks_RH_DorsAttn_Post_16" "7Networks_RH_DorsAttn_Post_17"
## [47] "7Networks_RH_DorsAttn_Post_18" "7Networks_RH_DorsAttn_FEF_1"
## [49] "7Networks_RH_DorsAttn_FEF_2" "7Networks_RH_SalVentAttn_FrOper_6"
## [51] "7Networks_RH_SalVentAttn_FrOper_7" "7Networks_RH_SalVentAttn_FrOper_8"
## [53] "7Networks_RH_SalVentAttn_Med_2" "7Networks_RH_Cont_Par_2"
## [55] "7Networks_RH_Cont_Par_4" "7Networks_RH_Cont_Par_5"
## [57] "7Networks_RH_Cont_Par_6" "7Networks_RH_Cont_PFCl_8"
## [59] "7Networks_RH_Cont_Cing_1" "7Networks_RH_Default_Par_4"
## [61] "7Networks_RH_Default_Temp_1" "7Networks_RH_Default_Temp_2"
## [63] "7Networks_RH_Default_PFCm_3" "7Networks_RH_Default_PFCm_9"sig_LOO <- load_effect_LOO[,LOO_results$sig_corrected]
visual <- sig_LOO[,c(1:7,34:39)]
dorsal_attn <- sig_LOO[,c(8:14,40:49)]
ventral_attn <- sig_LOO[,c(15:17,50:53)]
FPCN <- sig_LOO[,c(18:23,54:59)]
DMN <- sig_LOO[,c(25:33,61:64)]
network_avg_LOO <- data.frame(visual=rowMeans(visual), DAN = rowMeans(dorsal_attn), VAN = rowMeans(ventral_attn), FPCN = rowMeans(FPCN), DMN = rowMeans(DMN))LOO - all subjects
data_for_plot <- data.frame(network_avg_LOO, PTID = constructs_fMRI$PTID, BPRS = p200_clinical_zscores$BPRS_TOT[p200_clinical_zscores$PTID %in% constructs_fMRI$PTID], L3_acc = p200_data$XDFR_MRI_ACC_L3[p200_data$PTID %in% constructs_fMRI$PTID], omnibus_span = constructs_fMRI$omnibus_span_no_DFR_MRI)
data_for_plot$BPRS[data_for_plot$BPRS > 4] <- NA
plot_list <- list()
for (network in seq.int(1,5)){
for (measure in seq.int(7,9)){
plot_list[[colnames(data_for_plot)[network]]][[colnames(data_for_plot)[measure]]] <- ggplot(data=data_for_plot, aes_string(x= colnames(data_for_plot)[measure], y = colnames(data_for_plot)[network]))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()
}
}DAN/L3 acc, trend with BPRS DMN with L3 acc
plot_list[["visual"]][["omnibus_span"]]+ plot_list[["visual"]][["L3_acc"]] + plot_list[["visual"]][["BPRS"]] +
plot_layout(ncol=2)+
plot_annotation("Average temporal ISC in Visual ROIs")## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
for (measure in seq.int(7,9)){
print(colnames(data_for_plot)[measure])
print(cor.test(data_for_plot$visual, data_for_plot[,measure]))
}## [1] "BPRS"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$visual and data_for_plot[, measure]
## t = -0.77458, df = 167, p-value = 0.4397
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.20890473 0.09195905
## sample estimates:
## cor
## -0.05983157
##
## [1] "L3_acc"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$visual and data_for_plot[, measure]
## t = 1.7426, df = 168, p-value = 0.08323
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01762173 0.27818115
## sample estimates:
## cor
## 0.1332459
##
## [1] "omnibus_span"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$visual and data_for_plot[, measure]
## t = 0.13039, df = 168, p-value = 0.8964
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1406681 0.1603306
## sample estimates:
## cor
## 0.01005913plot_list[["FPCN"]][["omnibus_span"]]+ plot_list[["FPCN"]][["L3_acc"]] + plot_list[["FPCN"]][["BPRS"]] +
plot_layout(ncol=2)+
plot_annotation("Average temporal ISC in FPCN ROIs")## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
for (measure in seq.int(7,9)){
print(colnames(data_for_plot)[measure])
print(cor.test(data_for_plot$FPCN, data_for_plot[,measure]))
}## [1] "BPRS"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$FPCN and data_for_plot[, measure]
## t = -1.7987, df = 167, p-value = 0.07387
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.28293088 0.01337949
## sample estimates:
## cor
## -0.137859
##
## [1] "L3_acc"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$FPCN and data_for_plot[, measure]
## t = 1.1208, df = 168, p-value = 0.264
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06521038 0.23363422
## sample estimates:
## cor
## 0.08614944
##
## [1] "omnibus_span"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$FPCN and data_for_plot[, measure]
## t = 1.011, df = 168, p-value = 0.3135
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07360969 0.22563932
## sample estimates:
## cor
## 0.07776616plot_list[["DMN"]][["omnibus_span"]]+ plot_list[["DMN"]][["L3_acc"]] + plot_list[["DMN"]][["BPRS"]] +
plot_layout(ncol=2)+
plot_annotation("Average temporal ISC in DMN ROIs")## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
for (measure in seq.int(7,9)){
print(colnames(data_for_plot)[measure])
print(cor.test(data_for_plot$DMN, data_for_plot[,measure]))
}## [1] "BPRS"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$DMN and data_for_plot[, measure]
## t = -0.090326, df = 167, p-value = 0.9281
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1577830 0.1441227
## sample estimates:
## cor
## -0.006989431
##
## [1] "L3_acc"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$DMN and data_for_plot[, measure]
## t = 2.3467, df = 168, p-value = 0.02011
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02840307 0.32008683
## sample estimates:
## cor
## 0.1781557
##
## [1] "omnibus_span"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$DMN and data_for_plot[, measure]
## t = 1.0827, df = 168, p-value = 0.2805
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06812651 0.23086306
## sample estimates:
## cor
## 0.0832413plot_list[["DAN"]][["omnibus_span"]]+ plot_list[["DAN"]][["L3_acc"]] + plot_list[["DAN"]][["BPRS"]] +
plot_layout(ncol=2)+
plot_annotation("Average temporal ISC in DAN ROIs")## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
for (measure in seq.int(7,9)){
print(colnames(data_for_plot)[measure])
print(cor.test(data_for_plot$DAN, data_for_plot[,measure]))
}## [1] "BPRS"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$DAN and data_for_plot[, measure]
## t = -1.8703, df = 167, p-value = 0.06319
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.287971161 0.007892701
## sample estimates:
## cor
## -0.143238
##
## [1] "L3_acc"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$DAN and data_for_plot[, measure]
## t = 2.9392, df = 168, p-value = 0.003754
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07306722 0.35969089
## sample estimates:
## cor
## 0.2211494
##
## [1] "omnibus_span"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$DAN and data_for_plot[, measure]
## t = 1.5391, df = 168, p-value = 0.1257
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03318511 0.26375122
## sample estimates:
## cor
## 0.1179181plot_list[["VAN"]][["omnibus_span"]]+ plot_list[["VAN"]][["L3_acc"]] + plot_list[["VAN"]][["BPRS"]] +
plot_layout(ncol=2)+
plot_annotation("Average temporal ISC in VAN ROIs")## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
for (measure in seq.int(7,9)){
print(colnames(data_for_plot)[measure])
print(cor.test(data_for_plot$VAN, data_for_plot[,measure]))
}## [1] "BPRS"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$VAN and data_for_plot[, measure]
## t = -1.6153, df = 167, p-value = 0.1081
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.26993505 0.02744475
## sample estimates:
## cor
## -0.1240291
##
## [1] "L3_acc"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$VAN and data_for_plot[, measure]
## t = 0.20117, df = 168, p-value = 0.8408
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1353115 0.1656461
## sample estimates:
## cor
## 0.01551879
##
## [1] "omnibus_span"
##
## Pearson's product-moment correlation
##
## data: data_for_plot$VAN and data_for_plot[, measure]
## t = 1.464, df = 168, p-value = 0.1451
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03893637 0.25838497
## sample estimates:
## cor
## 0.1122356Pairwise
high_pairwise_sig <- high_correct_ISC_pairwise[,,LOO_results$sig_corrected]
low_pairwise_sig <- low_correct_ISC_pairwise[,,LOO_results$sig_corrected]
load_effect_pairwise <- high_pairwise_sig - low_pairwise_sig
visual_pairwise_L3 <- high_pairwise_sig[,,c(1:7,34:39)]
dorsal_attn_pairwise_L3 <- high_pairwise_sig[,,c(8:14,40:49)]
ventral_attn_pairwise_L3 <- high_pairwise_sig[,,c(15:17,50:53)]
FPCN_pairwise_L3 <- high_pairwise_sig[,,c(18:23,54:59)]
DMN_pairwise_L3 <- high_pairwise_sig[,,c(25:33,61:64)]
network_pairwise_high_load <- list(visual = apply(visual_pairwise_L3, c(1,2), mean), FPCN = apply(FPCN_pairwise_L3, c(1,2), mean), DMN = apply(DMN_pairwise_L3, c(1,2), mean), DAN = apply(dorsal_attn_pairwise_L3, c(1,2), mean), VAN = apply(ventral_attn_pairwise_L3, c(1,2), mean))
visual_pairwise <- load_effect_pairwise[,,c(1:7,34:39)]
dorsal_attn_pairwise <- load_effect_pairwise[,,c(8:14,40:49)]
ventral_attn_pairwise <- load_effect_pairwise[,,c(15:17,50:53)]
FPCN_pairwise <- load_effect_pairwise[,,c(18:23,54:59)]
DMN_pairwise <- load_effect_pairwise[,,c(25:33,61:64)]
network_pairwise <- list(visual = apply(visual_pairwise, c(1,2), mean), FPCN = apply(FPCN_pairwise, c(1,2), mean),
DMN = apply(DMN_pairwise, c(1,2), mean), DAN = apply(dorsal_attn_pairwise, c(1,2), mean),
VAN = apply(ventral_attn_pairwise, c(1,2), mean))# reorder based on span
span_order <- order(constructs_fMRI$omnibus_span_no_DFR_MRI)
network_pairwise_span_ordered_high_load <- list()
network_pairwise_span_ordered <- list()
for (network in seq.int(1,5)){
network_pairwise_span_ordered_high_load[[network]] <- network_pairwise_high_load[[network]][span_order, span_order]
network_pairwise_span_ordered[[network]] <- network_pairwise[[network]][span_order, span_order]
}network_graphs_high_load <- list()
for (network in seq.int(1,5)){
data <- data.frame(network_pairwise_span_ordered_high_load[[network]][,])
rownames(data) <- c(1:170)
colnames(data) <- c(1:170)
data %>%
# Data wrangling
as_tibble() %>%
rowid_to_column(var="X") %>%
gather(key="Y", value="Z", -1) %>%
# Change Y to numeric
mutate(Y=as.numeric(gsub("V","",Y))) -> mutated_data
#
ggplot(data=mutated_data,aes(X, Y, fill= Z)) +
geom_tile() +
scale_y_continuous(breaks = c(0,50,100,150),labels=c(0,50,100,150))+
geom_hline(yintercept=57,color="black")+
geom_hline(yintercept=114,color="black")+
geom_vline(xintercept=57,color="black")+
geom_vline(xintercept=114,color="black")+
scale_fill_gradient2()+
theme(aspect=1)+
labs(fill="ISC")+
ggtitle(paste("network:",names(network_pairwise)[network]))-> network_graphs_high_load[[network]]
}network_graphs_load_effect <- list()
for (network in seq.int(1,5)){
data <- data.frame(network_pairwise_span_ordered[[network]][,])
rownames(data) <- c(1:170)
colnames(data) <- c(1:170)
data %>%
# Data wrangling
as_tibble() %>%
rowid_to_column(var="X") %>%
gather(key="Y", value="Z", -1) %>%
# Change Y to numeric
mutate(Y=as.numeric(gsub("V","",Y))) -> mutated_data
#
ggplot(data=mutated_data,aes(X, Y, fill= Z)) +
geom_tile() +
scale_y_continuous(breaks = c(0,50,100,150),labels=c(0,50,100,150))+
geom_hline(yintercept=57,color="black")+
geom_hline(yintercept=114,color="black")+
geom_vline(xintercept=57,color="black")+
geom_vline(xintercept=114,color="black")+
scale_fill_gradient2()+
theme(aspect=1)+
labs(fill="Load Effect")+
ggtitle(paste("network:",names(network_pairwise)[network]))-> network_graphs_load_effect[[network]]
}High Load
Visualize pairwise comparisons
network_graphs_high_load[[1]] 
network_graphs_high_load[[2]] 
network_graphs_high_load[[3]] 
network_graphs_high_load[[4]] 
network_graphs_high_load[[5]] 
t_test_res = data.frame(matrix(nrow=5,ncol=2))
colnames(t_test_res) <- c("t value","p value")
cols <- c("low_within","low_across","med_within","med_across","high_within","high_across")
group_means <- data.frame(matrix(nrow=5,ncol=6))
colnames(group_means) <- cols
group_se <- data.frame(matrix(nrow=5,ncol=6))
colnames(group_se) <- cols
avg_over_groups<- list(mean=data.frame(within = matrix(nrow=5,ncol=1),across = matrix(nrow=5,ncol=1)),
se=data.frame(within = matrix(nrow=5,ncol=1),across = matrix(nrow=5,ncol=1)))
split_list <- list()
all_ISC_list_high_load <- list()
for (network in seq.int(1:5)){
# define dataframes
comps <- data.frame(within = matrix(nrow=170,ncol=1),across = matrix(nrow=170,ncol=1))
split_by_groups <- data.frame(matrix(nrow=56,ncol=6))
colnames(split_by_groups) <- cols
# loop over all subjects and make comparisons
for (suj in c(1:56, 58:113, 115:170)){
if (suj < 57){
comps$within[suj] <- mean(network_pairwise_span_ordered_high_load[[network]][1:56,suj],na.rm=TRUE)
comps$across[suj] <- mean(network_pairwise_span_ordered_high_load[[network]][c(58:113,115:170),suj],na.rm=TRUE)
}else if (suj > 57 & suj < 114){
comps$within[suj] <- mean(network_pairwise_span_ordered_high_load[[network]][58:113,suj],na.rm=TRUE)
comps$across[suj] <- mean(network_pairwise_span_ordered_high_load[[network]][c(1:56,115:170),suj],na.rm=TRUE)
}else if (suj > 114){
comps$within[suj] <- mean(network_pairwise_span_ordered_high_load[[network]][115:170,suj],na.rm=TRUE)
comps$across[suj] <- mean(network_pairwise_span_ordered_high_load[[network]][c(1:56,58:113),suj],na.rm=TRUE)}
}
all_ISC_list_high_load[[names(network_pairwise)[network]]] <- comps
# average over groups
avg_over_groups[["mean"]]$within[network] <- mean(comps$within, na.rm=TRUE)
avg_over_groups[["mean"]]$across[network] <- mean(comps$across, na.rm=TRUE)
avg_over_groups[["se"]]$within[network] <- se(comps$within)
avg_over_groups[["se"]]$across[network] <- se(comps$across)
avg_over_groups[["mean"]]$difference[network] <- avg_over_groups[["mean"]]$within[network] - avg_over_groups[["mean"]]$across[network]
avg_over_groups[["se"]]$difference[network] <- se(comps$within - comps$across)
# split by groups
split_by_groups$low_across <- comps$across[1:56]
split_by_groups$low_within <- comps$within[1:56]
split_by_groups$med_across <- comps$across[58:113]
split_by_groups$med_within <- comps$within[58:113]
split_by_groups$high_across <- comps$across[115:170]
split_by_groups$high_within <- comps$within[115:170]
split_list[[names(network_pairwise)[network]]] <- split_by_groups
group_means[network,] <- colMeans(split_by_groups)
for (group in seq.int(1,6)){
group_se[network,group] <- se(split_by_groups[,group])
}
temp2 <- t.test(comps$within,comps$across,paired=TRUE,var.equal = FALSE)
t_test_res[network,] <- c(temp2$statistic,temp2$p.value)
}
rownames(t_test_res) <- names(network_pairwise) print(t_test_res)## t value p value
## visual -5.0995924 9.155685e-07
## FPCN 1.7065772 8.975943e-02
## DMN 2.4082910 1.711611e-02
## DAN -3.1638297 1.850336e-03
## VAN -0.3083997 7.581626e-01bar_list <- list()
bar_plot_data <- data.frame(matrix(nrow = 30, ncol=7))
colnames(bar_plot_data) <- c("mean", "se", "network_name", "comparison", "WMC", "err_min", "err_max")
comparison_list <- c("within", "across")
WMC_list <- c("low", "med", "high")
row_count=1
#row for mean, se, network, comparison, WMC
for (network in seq.int(1,5)){
for (WMC in seq.int(1,3)){
for (comparison in seq.int(1,2)){
col_to_look <- ((WMC-1)*2+1) + (comparison-1)
bar_plot_data$mean[row_count] <- mean(split_list[[network]][,col_to_look])
bar_plot_data$se[row_count] <- group_se[network,col_to_look]
bar_plot_data$err_min[row_count] <- bar_plot_data$mean[row_count] - bar_plot_data$se[row_count]
bar_plot_data$err_max[row_count] <- bar_plot_data$mean[row_count] + bar_plot_data$se[row_count]
bar_plot_data$network_name[row_count] <- names(network_pairwise)[network]
bar_plot_data$comparison[row_count] <- comparison_list[comparison]
bar_plot_data$WMC[row_count] <- WMC_list[WMC]
row_count = row_count+1
}
}
bar_plot_data$comparison <- as.factor(bar_plot_data$comparison)
bar_plot_data$WMC <- factor(bar_plot_data$WMC, levels=c("low", "med", "high"))
bar_plot_data %>%
filter(network_name == names(network_pairwise)[network]) %>%
ggplot(aes(x=WMC, y= mean, fill=comparison))+
geom_bar(stat="identity", position="dodge")+
geom_errorbar(aes(ymin=err_min, ymax=err_max), position=position_dodge(0.9), width=0.2)+
ylab("Mean ISC")+
ggtitle(paste0("network:", names(network_pairwise)[network])) -> bar_list[[names(network_pairwise)[network]]]
}Average over subjects
(bar_list[["visual"]] + bar_list[["FPCN"]]) +
plot_layout(guides="collect")
(bar_list[["DMN"]]+ bar_list[["DAN"]])+
plot_layout(guides="collect")
bar_list[["VAN"]] 
#### Compare to behavior
VAN - both to acc; trend to span within DAN - both to acc; trend to span within DMN - both to acc; across to span FPCN: across to acc; within to span visual: both to acc
for (network in seq.int(1,5)){
data_to_plot <- data.frame(constructs_fMRI[span_order, c(1,7)], all_ISC_list_high_load[[network]])
data_to_plot <- merge(data_to_plot, p200_clinical_zscores[,c(1,2,8)], by="PTID")
data_to_plot <- merge(data_to_plot, p200_data[,c(1,7)], by="PTID")
data_to_plot$BPRS_TOT[data_to_plot$BPRS_TOT > 4] <- NA
print(names(network_pairwise)[network])
print(ggplot(data = data_to_plot)+
geom_point(aes(x=omnibus_span_no_DFR_MRI,y=within), fill="black")+
geom_smooth(aes(x=omnibus_span_no_DFR_MRI,y=within), method="lm", color="black")+
geom_point(aes(x=omnibus_span_no_DFR_MRI,y=across), color="red")+
geom_smooth(aes(x=omnibus_span_no_DFR_MRI,y=across), method="lm", color="red")+
ylab("ISC")+
ggtitle(paste0("network: "), names(network_pairwise)[network]))
print(cor.test(data_to_plot$omnibus_span_no_DFR_MRI, data_to_plot$within))
print(cor.test(data_to_plot$omnibus_span_no_DFR_MRI, data_to_plot$across))
print(ggplot(data = data_to_plot)+
geom_point(aes(x=BPRS_TOT,y=within), fill="black")+
geom_smooth(aes(x=BPRS_TOT,y=within), method="lm", color="black")+
geom_point(aes(x=BPRS_TOT,y=across), color="red")+
geom_smooth(aes(x=BPRS_TOT,y=across), method="lm", color="red")+
ylab("ISC")+
ggtitle(paste0("network: "), names(network_pairwise)[network]))
print(cor.test(data_to_plot$BPRS_TOT, data_to_plot$within))
print(cor.test(data_to_plot$BPRS_TOT, data_to_plot$across))
print(ggplot(data = data_to_plot)+
geom_point(aes(x=XDFR_MRI_ACC_L3,y=within), fill="black")+
geom_smooth(aes(x=XDFR_MRI_ACC_L3,y=within), method="lm", color="black")+
geom_point(aes(x=XDFR_MRI_ACC_L3,y=across), color="red")+
geom_smooth(aes(x=XDFR_MRI_ACC_L3,y=across), method="lm", color="red")+
ylab("ISC")+
ggtitle(paste0("network: "), names(network_pairwise)[network]))
print(cor.test(data_to_plot$XDFR_MRI_ACC_L3, data_to_plot$within))
print(cor.test(data_to_plot$XDFR_MRI_ACC_L3, data_to_plot$across))
}## [1] "visual"
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$within
## t = 1.6012, df = 166, p-value = 0.1112
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02861651 0.26970129
## sample estimates:
## cor
## 0.1233276
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$across
## t = 0.65654, df = 166, p-value = 0.5124
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1012987 0.2007545
## sample estimates:
## cor
## 0.05089162
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$within
## t = -1.2962, df = 165, p-value = 0.1967
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.24847402 0.05226153
## sample estimates:
## cor
## -0.1003989
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$across
## t = -1.062, df = 165, p-value = 0.2898
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.23136068 0.07035191
## sample estimates:
## cor
## -0.08239195
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$within
## t = 4.8281, df = 166, p-value = 3.11e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2106881 0.4769721
## sample estimates:
## cor
## 0.350904
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$across
## t = 4.3218, df = 166, p-value = 2.659e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1750428 0.4478680
## sample estimates:
## cor
## 0.3180239
##
## [1] "FPCN"
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$within
## t = 3.5301, df = 166, p-value = 0.0005378
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1175399 0.3996663
## sample estimates:
## cor
## 0.2642469
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$across
## t = 0.72207, df = 166, p-value = 0.4713
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09626941 0.20562368
## sample estimates:
## cor
## 0.055956
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$within
## t = -0.23681, df = 165, p-value = 0.8131
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1698207 0.1338057
## sample estimates:
## cor
## -0.01843248
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$across
## t = -0.49557, df = 165, p-value = 0.6209
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1893063 0.1139799
## sample estimates:
## cor
## -0.03855101
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$within
## t = 1.7854, df = 166, p-value = 0.07602
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01444652 0.28279595
## sample estimates:
## cor
## 0.1372635
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$across
## t = 2.284, df = 166, p-value = 0.02364
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02376943 0.31756789
## sample estimates:
## cor
## 0.174551
##
## [1] "DMN"
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$within
## t = 1.3469, df = 166, p-value = 0.1798
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04819519 0.25142502
## sample estimates:
## cor
## 0.1039733
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$across
## t = -0.3589, df = 166, p-value = 0.7201
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1785022 0.1240880
## sample estimates:
## cor
## -0.02784499
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$within
## t = -1.2239, df = 165, p-value = 0.2227
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.24321220 0.05784485
## sample estimates:
## cor
## -0.09485197
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$across
## t = -1.1393, df = 165, p-value = 0.2562
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.23702809 0.06438283
## sample estimates:
## cor
## -0.08834454
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$within
## t = 3.6711, df = 166, p-value = 0.0003253
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1279205 0.4084847
## sample estimates:
## cor
## 0.2740229
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$across
## t = 3.9015, df = 166, p-value = 0.0001386
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1447645 0.4226834
## sample estimates:
## cor
## 0.2898217
##
## [1] "DAN"
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$within
## t = 1.8431, df = 166, p-value = 0.06709
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01001055 0.28687263
## sample estimates:
## cor
## 0.1416139
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$across
## t = 0.34612, df = 166, p-value = 0.7297
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1250638 0.1775424
## sample estimates:
## cor
## 0.02685449
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$within
## t = -0.44108, df = 165, p-value = 0.6597
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1852164 0.1181613
## sample estimates:
## cor
## -0.0343181
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$across
## t = -0.31544, df = 165, p-value = 0.7528
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1757579 0.1277905
## sample estimates:
## cor
## -0.02454957
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$within
## t = 4.4445, df = 166, p-value = 1.605e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1837685 0.4550463
## sample estimates:
## cor
## 0.3261046
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$across
## t = 4.2078, df = 166, p-value = 4.212e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1668847 0.4411247
## sample estimates:
## cor
## 0.31045
##
## [1] "VAN"
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$within
## t = 1.921, df = 166, p-value = 0.05645
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.004034671 0.292347552
## sample estimates:
## cor
## 0.1474652
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$across
## t = 0.99438, df = 166, p-value = 0.3215
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07533739 0.22572996
## sample estimates:
## cor
## 0.07695015
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$within
## t = -0.64558, df = 165, p-value = 0.5194
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2005299 0.1024497
## sample estimates:
## cor
## -0.05019486
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$across
## t = -0.56907, df = 165, p-value = 0.5701
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1948131 0.1083328
## sample estimates:
## cor
## -0.04425894
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$within
## t = 1.9777, df = 166, p-value = 0.04962
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.0003166685 0.2963219344
## sample estimates:
## cor
## 0.1517192
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$across
## t = 2.2328, df = 166, p-value = 0.0269
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.01985964 0.31404637
## sample estimates:
## cor
## 0.170756Load Effects
Visualize pairs
network_graphs_load_effect[[1]] 
network_graphs_load_effect[[2]] 
network_graphs_load_effect[[3]] 
network_graphs_load_effect[[4]] 
network_graphs_load_effect[[5]] 
t_test_res = data.frame(matrix(nrow=5,ncol=2))
colnames(t_test_res) <- c("t value","p value")
cols <- c("low_within","low_across","med_within","med_across","high_within","high_across")
group_means <- data.frame(matrix(nrow=5,ncol=6))
colnames(group_means) <- cols
group_se <- data.frame(matrix(nrow=5,ncol=6))
colnames(group_se) <- cols
avg_over_groups<- list(mean=data.frame(within = matrix(nrow=5,ncol=1),across = matrix(nrow=5,ncol=1)),
se=data.frame(within = matrix(nrow=5,ncol=1),across = matrix(nrow=5,ncol=1)))
split_list <- list()
all_ISC_list_LE <- list()
for (network in seq.int(1:5)){
# define dataframes
comps <- data.frame(within = matrix(nrow=170,ncol=1),across = matrix(nrow=170,ncol=1))
split_by_groups <- data.frame(matrix(nrow=56,ncol=6))
colnames(split_by_groups) <- cols
# loop over all subjects and make comparisons
for (suj in c(1:56, 58:113, 115:170)){
if (suj < 57){
comps$within[suj] <- mean(network_pairwise_span_ordered[[network]][1:56,suj],na.rm=TRUE)
comps$across[suj] <- mean(network_pairwise_span_ordered[[network]][c(58:113,115:170),suj],na.rm=TRUE)
}else if (suj > 57 & suj < 114){
comps$within[suj] <- mean(network_pairwise_span_ordered[[network]][58:113,suj],na.rm=TRUE)
comps$across[suj] <- mean(network_pairwise_span_ordered[[network]][c(1:56,115:170),suj],na.rm=TRUE)
}else if (suj > 114){
comps$within[suj] <- mean(network_pairwise_span_ordered[[network]][115:170,suj],na.rm=TRUE)
comps$across[suj] <- mean(network_pairwise_span_ordered[[network]][c(1:56,58:113),suj],na.rm=TRUE)
}
}
all_ISC_list_LE[[names(network_pairwise)[network]]] <- comps
# average over groups
avg_over_groups[["mean"]]$within[network] <- mean(comps$within, na.rm=TRUE)
avg_over_groups[["mean"]]$across[network] <- mean(comps$across, na.rm=TRUE)
avg_over_groups[["se"]]$within[network] <- se(comps$within)
avg_over_groups[["se"]]$across[network] <- se(comps$across)
avg_over_groups[["mean"]]$difference[network] <- avg_over_groups[["mean"]]$within[network] - avg_over_groups[["mean"]]$across[network]
avg_over_groups[["se"]]$difference[network] <- se(comps$within - comps$across)
# split by groups
split_by_groups$low_across <- comps$across[1:56]
split_by_groups$low_within <- comps$within[1:56]
split_by_groups$med_across <- comps$across[58:113]
split_by_groups$med_within <- comps$within[58:113]
split_by_groups$high_across <- comps$across[115:170]
split_by_groups$high_within <- comps$within[115:170]
split_list[[names(network_pairwise)[network]]] <- split_by_groups
group_means[network,] <- colMeans(split_by_groups)
for (group in seq.int(1,6)){
group_se[network,group] <- se(split_by_groups[,group])
}
temp2 <- t.test(comps$within,comps$across,paired=TRUE,var.equal = FALSE)
t_test_res[network,] <- c(temp2$statistic,temp2$p.value)
}
rownames(t_test_res) <- names(network_pairwise) print(t_test_res)## t value p value
## visual -1.0877393 0.278277558
## FPCN 3.3130726 0.001131379
## DMN 1.1234601 0.262854555
## DAN -0.8727005 0.384079386
## VAN 1.6959284 0.091762824Compare across groups
bar_list <- list()
bar_plot_data <- data.frame(matrix(nrow = 30, ncol=7))
colnames(bar_plot_data) <- c("mean", "se", "network_name", "comparison", "WMC", "err_min", "err_max")
comparison_list <- c("within", "across")
WMC_list <- c("low", "med", "high")
row_count=1
#row for mean, se, network, comparison, WMC
for (network in seq.int(1,5)){
for (WMC in seq.int(1,3)){
for (comparison in seq.int(1,2)){
col_to_look <- ((WMC-1)*2+1) + (comparison-1)
bar_plot_data$mean[row_count] <- mean(split_list[[network]][,col_to_look])
bar_plot_data$se[row_count] <- group_se[network,col_to_look]
bar_plot_data$err_min[row_count] <- bar_plot_data$mean[row_count] - bar_plot_data$se[row_count]
bar_plot_data$err_max[row_count] <- bar_plot_data$mean[row_count] + bar_plot_data$se[row_count]
bar_plot_data$network_name[row_count] <- names(network_pairwise)[network]
bar_plot_data$comparison[row_count] <- comparison_list[comparison]
bar_plot_data$WMC[row_count] <- WMC_list[WMC]
row_count = row_count+1
}
}
bar_plot_data$comparison <- as.factor(bar_plot_data$comparison)
bar_plot_data$WMC <- factor(bar_plot_data$WMC, levels=c("low", "med", "high"))
bar_plot_data %>%
filter(network_name == names(network_pairwise)[network]) %>%
ggplot(aes(x=WMC, y= mean, fill=comparison))+
geom_bar(stat="identity", position="dodge")+
geom_errorbar(aes(ymin=err_min, ymax=err_max), position=position_dodge(0.9), width=0.2)+
ylab("Mean ISC")+
ggtitle(paste0("network:", names(network_pairwise)[network])) -> bar_list[[names(network_pairwise)[network]]]
}(bar_list[["visual"]] + bar_list[["FPCN"]]) +
plot_layout(guides="collect")
(bar_list[["DMN"]]+ bar_list[["DAN"]])+
plot_layout(guides="collect")
bar_list[["VAN"]] 
Relate to behavior
visual with acc within FPCN with span/within DMN with acc within and across DAN with span withi; with acc both; both with BPRS if outlier removed VAN within with span
for (network in seq.int(1,5)){
data_to_plot <- data.frame(constructs_fMRI[span_order, c(1,7)], all_ISC_list_LE[[network]])
data_to_plot <- merge(data_to_plot, p200_clinical_zscores[,c(1,2,8)], by="PTID")
data_to_plot <- merge(data_to_plot, p200_data[,c(1,7)], by="PTID")
data_to_plot$BPRS_TOT[data_to_plot$BPRS_TOT > 4] <- NA
print(names(network_pairwise)[network])
print(ggplot(data = data_to_plot)+
geom_point(aes(x=omnibus_span_no_DFR_MRI,y=within), fill="black")+
geom_smooth(aes(x=omnibus_span_no_DFR_MRI,y=within), method="lm", color="black")+
geom_point(aes(x=omnibus_span_no_DFR_MRI,y=across), color="red")+
geom_smooth(aes(x=omnibus_span_no_DFR_MRI,y=across), method="lm", color="red")+
ylab("ISC")+
ggtitle(paste0("network: "), names(network_pairwise)[network]))
print(cor.test(data_to_plot$omnibus_span_no_DFR_MRI, data_to_plot$within))
print(cor.test(data_to_plot$omnibus_span_no_DFR_MRI, data_to_plot$across))
print(ggplot(data = data_to_plot)+
geom_point(aes(x=BPRS_TOT,y=within), fill="black")+
geom_smooth(aes(x=BPRS_TOT,y=within), method="lm", color="black")+
geom_point(aes(x=BPRS_TOT,y=across), color="red")+
geom_smooth(aes(x=BPRS_TOT,y=across), method="lm", color="red")+
ylab("ISC")+
ggtitle(paste0("network: "), names(network_pairwise)[network]))
print(cor.test(data_to_plot$BPRS_TOT, data_to_plot$within))
print(cor.test(data_to_plot$BPRS_TOT, data_to_plot$across))
print(ggplot(data = data_to_plot)+
geom_point(aes(x=XDFR_MRI_ACC_L3,y=within), fill="black")+
geom_smooth(aes(x=XDFR_MRI_ACC_L3,y=within), method="lm", color="black")+
geom_point(aes(x=XDFR_MRI_ACC_L3,y=across), color="red")+
geom_smooth(aes(x=XDFR_MRI_ACC_L3,y=across), method="lm", color="red")+
ylab("ISC")+
ggtitle(paste0("network: "), names(network_pairwise)[network]))
print(cor.test(data_to_plot$XDFR_MRI_ACC_L3, data_to_plot$within))
print(cor.test(data_to_plot$XDFR_MRI_ACC_L3, data_to_plot$across))
}## [1] "visual"
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$within
## t = 0.72347, df = 166, p-value = 0.4704
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09616224 0.20572728
## sample estimates:
## cor
## 0.05606383
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$across
## t = 0.077256, df = 166, p-value = 0.9385
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1455458 0.1572631
## sample estimates:
## cor
## 0.005996103
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$within
## t = -1.2377, df = 165, p-value = 0.2176
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.24421648 0.05678066
## sample estimates:
## cor
## -0.09590996
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$across
## t = -1.2493, df = 165, p-value = 0.2133
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.24506491 0.05588109
## sample estimates:
## cor
## -0.09680401
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$within
## t = 2.2592, df = 166, p-value = 0.02517
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02187271 0.31586054
## sample estimates:
## cor
## 0.1727105
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$across
## t = 1.6242, df = 166, p-value = 0.1062
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02684343 0.27134588
## sample estimates:
## cor
## 0.1250747
##
## [1] "FPCN"
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$within
## t = 2.5448, df = 166, p-value = 0.01184
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.04364479 0.33534507
## sample estimates:
## cor
## 0.1937741
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$across
## t = 0.57111, df = 166, p-value = 0.5687
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1078495 0.1943898
## sample estimates:
## cor
## 0.04428339
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$within
## t = -1.3264, df = 165, p-value = 0.1866
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.25066394 0.04993225
## sample estimates:
## cor
## -0.1027102
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$across
## t = -1.7045, df = 165, p-value = 0.09017
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.27785354 0.02073744
## sample estimates:
## cor
## -0.1315404
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$within
## t = 1.0952, df = 166, p-value = 0.275
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06757768 0.23311896
## sample estimates:
## cor
## 0.08469874
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$across
## t = 1.1392, df = 166, p-value = 0.2563
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06419362 0.23633052
## sample estimates:
## cor
## 0.08807219
##
## [1] "DMN"
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$within
## t = 1.4721, df = 166, p-value = 0.1429
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.0385545 0.2604512
## sample estimates:
## cor
## 0.113518
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$across
## t = 0.083877, df = 166, p-value = 0.9333
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1450427 0.1577643
## sample estimates:
## cor
## 0.006510009
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$within
## t = 0.32537, df = 165, p-value = 0.7453
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1270299 0.1765070
## sample estimates:
## cor
## 0.02532216
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$across
## t = -0.53845, df = 165, p-value = 0.591
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1925209 0.1106858
## sample estimates:
## cor
## -0.04188179
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$within
## t = 2.1863, df = 166, p-value = 0.03019
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.0163018 0.3108348
## sample estimates:
## cor
## 0.1672987
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$across
## t = 2.3111, df = 166, p-value = 0.02206
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02583945 0.31942908
## sample estimates:
## cor
## 0.1765585
##
## [1] "DAN"
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$within
## t = 2.7707, df = 166, p-value = 0.006232
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.06076595 0.35049405
## sample estimates:
## cor
## 0.2102414
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$across
## t = 0.51, df = 166, p-value = 0.6107
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1125307 0.1898260
## sample estimates:
## cor
## 0.03955304
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$within
## t = -1.9661, df = 165, p-value = 0.05097
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2963526368 0.0005786435
## sample estimates:
## cor
## -0.1512982
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$across
## t = -2.1547, df = 165, p-value = 0.03264
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.30951640 -0.01391464
## sample estimates:
## cor
## -0.1654287
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$within
## t = 3.3413, df = 166, p-value = 0.00103
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1035562 0.3877038
## sample estimates:
## cor
## 0.25103
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$across
## t = 2.4909, df = 166, p-value = 0.01373
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03954032 0.33169084
## sample estimates:
## cor
## 0.1898137
##
## [1] "VAN"
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$within
## t = 2.4846, df = 166, p-value = 0.01396
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03906592 0.33126791
## sample estimates:
## cor
## 0.1893557
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span_no_DFR_MRI and data_to_plot$across
## t = 0.95383, df = 166, p-value = 0.3416
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07845706 0.22274954
## sample estimates:
## cor
## 0.07382977
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$within
## t = -1.6117, df = 165, p-value = 0.1089
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2712288 0.0278980
## sample estimates:
## cor
## -0.124493
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$BPRS_TOT and data_to_plot$across
## t = -1.6854, df = 165, p-value = 0.09379
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.27649686 0.02220636
## sample estimates:
## cor
## -0.130096
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$within
## t = 0.44308, df = 166, p-value = 0.6583
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1176528 0.1848173
## sample estimates:
## cor
## 0.03436926
##
##
## Pearson's product-moment correlation
##
## data: data_to_plot$XDFR_MRI_ACC_L3 and data_to_plot$across
## t = 0.4127, df = 166, p-value = 0.6804
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1199763 0.1825400
## sample estimates:
## cor
## 0.0320151